---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
  results:
  - metrics:
    - type: mean_reward
      value: 245.74 +/- 18.06
      name: mean_reward
    task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: LunarLander-v2
      type: LunarLander-v2
---

# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)

```python
import gym

from huggingface_sb3 import load_from_hub

from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env

env = make_vec_env('LunarLander-v2', n_envs=16)
model = PPO('MlpPolicy', env, verbose=1)

model.learn(total_timesteps=5 * 10**5)

eval_env = gym.make('LunarLander-v2')
mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=10, deterministic=True)
print(f"Reward mean: {mean_reward:.2f}, Reward STD: {std_reward:.2f}")
```